{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6cd9910",
   "metadata": {},
   "source": [
    "# Lab8 机器学习\n",
    "\n",
    "本笔记本是练习机器学习的基本操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fe4179",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbor 最近邻\n",
    "以下是使用 KNN 和交叉验证对豆子数据集进行分类的示例。 我们采用 sklearn 中的两个函数来完成分类任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9267d005",
   "metadata": {},
   "source": [
    "[sklearn.neighbors.KNeighborsClassifier()](https://scikit-learn.org/stable/modules/ generated/sklearn.neighbors.KNeighborsClassifier.html)是k近邻分类方法。 其重要参数有：\n",
    "- n_neighbors（默认=5）：默认情况下用于分类投票的邻居数量。\n",
    "- 权重（默认='uniform'）：预测中使用的权重函数（uniform：每个邻域中的所有点均等加权）。\n",
    "- metric（默认='minkowski'）：距离度量\n",
    "- p：Minkowski 度量的幂参数（p=1:manhattan_distance；p=2:euclidean_distance）\n",
    "\n",
    "<div align=\"center\">\n",
    "    <img src=\"img/dist-metrics.png\" width=\"60%\">\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a996e3e",
   "metadata": {},
   "source": [
    "### 交叉验证\n",
    "下图提醒您交叉验证是如何工作的：\n",
    "\n",
    "<div align=\"center\">\n",
    "\t<img src=\"img/k-cv.png\" width=\"70%\">\n",
    "</div>\n",
    "\n",
    "[sklearn.model_selection.cross_val_score()](https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)，用于通过交叉验证评估分数。 其重要参数有：\n",
    "- 估计器：用于拟合数据的对象，即您的模型。\n",
    "- x：拟合数据，即特征。\n",
    "- y：在监督学习的情况下尝试预测的目标。\n",
    "- 组（默认=无）：折叠数\n",
    "- 评分（默认=无，如果无，则使用估计器的默认评分器（如果可用）。用于评估的评分指标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "246da6b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Oh6GcZAxMnSo3Nap0TdoElZQ+9GOelNjFoZRSSqnBaZKWwPytsPsZeOchqFrdt6dm+VngToLutt5jHfWQnAVFi2Ifq1JKKaX60unOBNVcBc9/QzoBGJc0RM+ZARf+SEbRUvNhxa2w+sfQuh+wkFogxzw+p6NXSimllCZpCWrtvRDolI0BPep3wvbHYeHfy882BJfcBf4mcHkgd4a2dFJKKaXihSZpCai7Heo2Q0ZZ3+OpBbDraamN1rA7MsIWhnnXSOkNrUeolFJKxQ9N0hKQy92bgJmoYuA2JCNoGS2SwBkD4SBs+RPkzYKKs52LWSml1Ak6dAjq6mDSJMjLczoaNYo0SUtAnmSoOAcqX+1NxmwY2g7Kz+klvaNmLg8kZcCuv2qSppRS44rfD3fdBa+8Ir0zw2G48kr47GfBlUBrV6yFw4flPU6wJFSTtAS1/PPQdkhGzgySpE0+D2rfhv7dWlwemSJVSik1jvz+97BqFVRUSFIWCsH//A+UlsIHP+h0dKNjzx644w7Yt0/uL1gAX/0qFBU5GlasGJtgzRqXL19u161b53QYccFaKUzb2QBZFZBZBk99DjqbICWn95yWSlj2eZj7EWfjVUopNULhMPzd30FODiQl9R5vbZWq/w884Fxso6W1FT7zGQgGe0fQDh6EwkL41a8Spu2UMWa9tXb5YI8l0Hio6s8YKJgr05hZ5XL/jK+ADUJLNbQdgJYqKJgPMy51OlqllFIjFgrJdKfX2/d4UpIkN4ngrbegpQXy8+UXmDFQUgK1tbB5s9PRxURipKFqRKyF/Dnwofth3yvQXgdFC6H09EhLKKWUUuOD1wuLFsHOnTKy1OPwYemjmQjq6wcvO2AMNDfHPh4HaJI2AXS3w6Y/wO6/yW7OinPhlH+CtAKnI1NKKXXCPv95uPlmqK6GlBTo7ITcXPj4x52ObHTMmiWjC9b2JmvhsNyfNjH6xWqSluCshVe+Lw3V00ukJMfu52DrI5AzDdKLYf61shtU66QppWKhhkY2sZ8mOsgllUWUMYlsp8Maf6ZOhfvug+efl4X1c+bAypWQmel0ZKNjyRJYtgzWrpW1d+GwjKBdcYVslpgAHN04YIy5FPgF4AYesNbe3u/xTwM/BfZHDt1jrR12NaRuHOjryHZ49qbeUhzd7VCzWv4sXiq9Orsa4bQbYfaVTkerlEp01TTwKrvw4cWHhy4CdBPiAmZTQpbT4al44/dLErpqlUzxXnoprFiRUCVGhts44NhImjHGDdwLXATUAG8bY56w1m7rd+oj1tobYx5ggmivA0zvKFnTPgiHpJZauBuSs+XnjQ/L5gF30nDPppRSJ2cjNfjwkowshE0hCehmIzWapKmBfD4pJ5IoJUWOk5Op6GnAbmvtXmttN/An4CoH40lI6SVAZEofZNTMnSTHktLlmCcZgl3QUe9UlEqpicBiaaITX7/xAR9emuhwKCql4peTSVopUB11vyZyrL+rjTGbjDGPGWPKB3kcY8wNxph1xph1hw8fHotYx63cGTBpOTRXSiLmSQF/qyRoaZENQaGAFLRN1iUhSiWMTgJspIan2MQLvEcNjVicrYtpMGSRQjfBPsf9BMkmxaGoHHboEDz+ODzyiOzUTLDapTHR3Q2NjbJmLcE4maQNtky9/3+dTwJTrLWLgBeA3w32RNba+621y621ywsKdMtiNGPg3Ftg0fUQ8kNqnuzqzJsNuCRxa90Pcz4C3gn6GalUovET5Dm2sYX9dBOigQ5eZifvcdDp0FhMGZ0E8BPAYiNr0gIsHPQ7eoJ7/XUp1vrrX8Nvfwtf+hLcf78maiMVDMLvfgfXXgvXXw+f/rT8O00gTiZpNUD0yFgZUBt9grW23lrrj9z9DbAsRrElFE8yLP4kfPRP8Mln4eo/QkYptNZAoEPKcSy63ukolVKjZS+HacNPFqkk4SGVJDJIZhM1A0axYq2CXM5jJj48tNBFCl7OZxal5DgaV8x1dMDPfw7Z2bJTsaJC2jk9/jhs6780Ww3qD3+AP/5R/h2WlUnSdtttCVXo1skSHG8DM40xU5Hdm9cBn4g+wRhTYq09ELl7JfBebENMTEWL4LK7IdgB3lQwo5yqh0NQtwU6jkg7qtwZWt5DqVg6RCte3H2OuXFhgRa6yCfdmcAiKsilglwsFjPopMoEsG2b7FyMnv1xu2XX4ptvwvz5zsU2Hvj9ktBOmtTbFisjQ2rFPfYYLFzobHyjxLEkzVobNMbcCDyLlOB40Fq71RjzfWCdtfYJ4F+NMVcCQaAB+LRT8SaKcBC2/Q+89z+yNq1wASz7LOTNGp3n76iHl74DTZXS1B2kBtvZN2tXA6ViJQMftTT1OWYj/6QQP38RJ2yCBsP3nezf6kkN1NYGgUDfvqUA6emwf//g14xDjhYasdY+ba2dZa2dbq29LXLs1kiChrX2W9ba+dbaxdbaD1hrtzsZbyJ45yF45z9lA0FmOTTuhedvhpaa0Xn+dfdBc5U0c8+qkNeofBV2PT06z6+UOrYZFGIwdEXWfYWxtNBFGTmk4XM6PAUwb56M/ES3N+rulvVo557rXFzjRXa23Nrb+x5vbITFi52JaQwkTjU4dUz+VtjxhCROnmSZgkzNh1AQdjzZ91xroWE3bHkEtj0GLSP4YhLogJo3I2U/IoyBlDxN0pSKpSxS+ACzScJNK12042c6+ZzJybfSsZGErx3/sU+OZ9ZCZaVMLe7dG/vF+klJ8N3vSqP0mhpp7XT4sLR6miAtj06K2w2f/SwcOSK3jg4ZQUtJgauvdjq6UaNtoSaQzkgdNFe//9eT0qDx/d771kpx2y1/ki24FhmBO+PLMP2ioZ/f2sE/54wB6+xaZaUmnGIy+RCL6CSABxdJo/Bxf5hW3mQv7fixQAEZnMk00sfb6JzfDz/7mewEdLnkg2v5cvjWt+SXfKzMnQu//z1s3CgxzZ8PeXmxe/3x7vzzZTTtscegthYuugiuuUbWqSUITdImkNQC2SQQCvRdH9bdBvmze+837pEELWNSb0IX7II1v4TSU6WeWqhbNgeEuqFgHvgyJdkrPkWOpxfLddZC5xGY/U+xe59KKWEwpDI6bUQ66WYVO3DhIoMULJZ62niZHVzBwvG1vuyxx+CVV2DKlMi3SAtr1sB//ZeUxIil5GQ4/fTYvmYiWbJEbglKpzsnkKQ0mP8xKb3hb5VkrfUAJKXCrKiOG7XrAdN3xM2TLBsBDm2G+p3wv5+CVd+BV34Af7ke9jwn5532RUnYWqplbVprNRQuhNkfiulbVUqNsioaCBE6uvHAYEgnmRa6OEybw9Edp6eeguLi3m3nxsjoy1//qjXKVFzRkbQJZuEnZB3atkehswHKz4BFn4T0ot5z3F4GlhWO8vL3JGHLLJP7QT+s+YUUyM2eDB/6NexfK31Dc6ZB8ZKBU6xKqfGlkwBDfa/vIhDbYE5WV9fAaU23WxbuH0swCPv2yQ7MiorRqy/U0QH/+7/w7LNy/5JL4CMfgdTU0Xn+oVgLW7fC+vXyWmefnVDTheOd/uqcQMJBqHwNqt+A7Cmw9LNQetrAz5iyM+CdByNtpJLlmL9Vfna5wd8MGWW953t88ve8+nVJ0rypMOX8WL0rpVQsFJBBmNo+tc3CkW9zuaQ5GdrxO/98eO45KYDa49AhOOec4ZOud96Bn/wEWlrkQ2/KFLjllr7PcyLCYfje92RtWmGkX9/DD8v922+XdXNjwVq4+254+mlJUq2VCv433wznnTc2r6mOi053ThA2DK/fLre6rVC7AV7+rpTj6C9jkmwS6GqU0hwtNdJSasWt4PIyeEMvIyNqSqnEVEImJWTRTCedBOigmxY6mUPx+Ns4cP31kgxVVcGBA7KzMicH/mmYxbN1dZJIGSNJWVmZXPud78jo2snYskWq5E+eLKNZqany85YtchsrmzbB3/4G5eXyfsrLITcX7rxTSltYK/1EV6+Wf1cq5nQkbYI4tBmqV0PW5N4viuEQvPcXmHm5JGbRpl0Ik06VTQAut3Qp8KbKiJpx9R1ls2GwITlfKZWYXLhYwUz2Uc8+6nHjYgaFlJHtdGjHLy8P7r1Xko99+yQhOvtsSBtmRPC116R4akaG3DdGEr2aGukesGjRicdTUyMJUfQonjEywlZTc+znthZaWyW5G65Ibn9r1sj50SN1KSnQ0CDTn08+KUmi2y2xrFgBN92kxXZjSJO0CeLwVrCm72eAK9I1pn7nwCQNIDkLKs7ue8yXIaNsb9zR+5liQ7LxoHDB2MWvlHKeBzczKGQGhU6HcvJSU6Vkw0i1tAw97dh2jI0THR29rzmYgoLBn9vlgvz84Z/7jTfgN7+R6dqUFPjoR6Xh+EimSH0+Sb76s1Y2V2zZIqNrPQnjiy/CrFmyVk7FhCZp45y10H5IFuanDvN3OTkbzGCbAYzsxjweUy+QNlJVqyHYKSNoBfO0P6dS8aKDbhpox4ubAjJwjafyGPFq8WL485/7jnj1THPOGqKvXl0d/Md/wNq1cv/UU+GLX+xdd9ZjyRKZbqypgZJINfADB+TYKacMHdPmzfCDH0itsPJyqbX20EMS4yc+MfR1Pc49Fx55RK7zRaasGxshM1M2E0ya1PteXS5JJp96SpO0GNIkbRyr3wVv3iHlLqyVRGnmFZCUDvlzZNSrR/lZsOEB6GqShM1a6DwsiV3hCfShzSyDBdeO3ntRSp08i2UbB9hIzdFC1Em4OZ1plJKtydrJWLIEzjpLCuCmp0uC5vfDpz41+GhXICDFcevqoLRUjm3YAN/8JvzqV317Tnq98KMfwf33y/ODbGK44YbhpxYffVRGzzIj37R9PkmsHntMRtT697Xsb9o0uPFGuO++3hG1rCz4t3+Dr31t4Gic2y3vWcWMJmnjVFczrLoFwhbSS6G7RQrQbnlEph27OyBniuzinH6x7Ni84DZYfbvUSbPIY+d+SxufK5Uo6mjlXapJJ5kwYQ7SQjvd7KeJcnI4mxkUcZxD50q4XJJ0rV4t69OSk2W6dKj1Yhs2yGhYeXnvsUmTZJPChg1wxhl9z8/Lk+cPRMqZjGTdV03NwHV0Pp8kUu3tx07SAC6/XJLP996TaxcskOuWLZORuuLi3nMPH5bkT8WMJmnjVPUb0N0ufTgJw4EN4E6SArUNe6WI7P43ZVRt/f0w5yq49C646iEpMuvyQEapTlEqlUj2cgQ3blwYamgmSJhkPAQJ4yfES+zkShaNWheCCcfjkcXzK1Yc+9z6+sHXe4XD8lh/lZVSCqO2VkbtLrqod4RsKAsXwqpVfde6tbXJ9Oexro2WnQ1nntn32Oc/L6U4qqrkfQeDUnLkmmtG/rzqpGmSNk511nO0FEZXk+y2TEqDrlZpw2TckrR5ksHtg51PwbSLYN7VMoKmlEo8QUK4MHQRIEAoql9nGC9uuglSRT1zKHE0zgmhp9Bt9Bo2a2VErqKi77kbNkizdWtl+nLdOul+cMcdUhpkKB/9qIzq1dZK6Yz2dknSvvENmZo8GWVl8Otfy8jh/v0wY4aM/vnGWbmVcU7rpI1T+XMjpS9s5Mta5LMg1BX5HPBwtDt6T0unHf/ncNBKqTFVTi7dBAkSOnosRBg3Bh/yodDF0DW9/ASppYk6Wo8WqlUnaP58mTKsrJTEqa1Nfj7lFHmsRzgM99wj69xKSyXZqqiQqdInnhj+NcrL4a67pPBsOCxrzH74Q/jAB0bnPWRkwKWXwj//s4weaoIWczqSNk4VL4GSpVD7tkxphgPgD8hOza7GSFcn21vLDCuja0qpxFVBDuXkUEk9QUJY5LtaCVkAWMIUkjHotbupYx2VR5OzNHysYCbZjHFbokRljBS6feopeOYZOfaZz8CHPtR3QX5Dg5TP6N+1IDcX3nxTNiZE6+yEl16SGmf5+dI+6utfH9v3ohyjSdo4EeyCfa/AgXWQWiAbAc74CuxfA/tWSTLWsFfO7WyUDgG+TDne3QnGA/N0KYFSCc2Fi3OZyUwKWUcltTSTgQ+DoZlOysihOJKwRWugnbXsIxUfnsgESwfdvMouPsSio22gYqGDbqppxE+AIjIpJCOmrz+qfD64+mq5DSUlRRK6UKjvFKXfL0V2o3V0yO7QnTtl5K27W9ax3Xzz6DeWAGgAACAASURBVI2eqbiiSdo4EOiAF74pRWcxUL8DulshdyZMvwjO+ZaU0miugspXYNtfoOYNGf1uPyK7Nxf9A8y42Ol3opQaay4Mk8jmQ2RxkBb2cJggYSaTSwW5g5bh2Ec9Bo4maACpJNFCJ/W0k096TGI/SAsvs5MQYQywmf1UkMvZzEjc8iFpabByZW8vUZdLkq/WVvjwh/ueu2qVJGjRyVtnp3RPOOssnY4cTZWVsGePlCRZtMixLguapI0De1+UxCy9FKpelfVlSVnQdhBq18Gqb8Pl90JWBSz6pNwadsOe56QbwNSVsoZNd3IqNXEYDCVkHZ3qHE6AEGbQJcqGIIPsUDwGG5kyPZ4RsDBh3mA3XtxHe4FaLJU0MJlGKsg97jjGjc99TpKt1aslSXO7ZXflqf167a1d29uWqkdPG6fqalncr05OzxrBnilqgKIiuO02KaESY5qkjQPVqyUpaz8EoW7ZxQlSC82XBc3V0mOzeHHvNbkz5KaUUsdSRg67qMNijyZWAUK4MeQxTD/LfjroZiM17KMeF4YZFLCQ0qhdpkNrpJMuQmSSfPSYweDFTRUNkqS1tcHzz8t6rKIiuOwymDPn+N9wvElNhVtukTpkTU2ygWCwFlK5uQOLyfbsHhuu76gauddek3WEkyf3rh08eBB+9jPZbRvj0Q7d3TkOpOTKGrNgF/RsuOrZGODyyJ+dDcM/RygAR3bICJs9/i/GSqkE1Y6fNJKoIIdmOmmlixY66aSbM5iKl5HtOAoS4gW28z5HSCOJZLxs5yCvsuvoyNpwXJHt6P3PtYAbI9N/X/2qlIXYu1cWz990E7zyygm86zhVUAAzZw7d4/PSS6VeWWen3LdWymMsWdLbTkqdnGeekSnO6M0dRUWwfbsk0TGmI2njwIzL4P2XwJMi98MWgh2yDs3tk7+nw9U+O7gRXr9d1rFhIa0Yzvs25EyNRfRKqXjUQTdvsodDtAKQgpfFlOEnSBJuJpNHFikjfr79NNFGF5lR12SSQh2tI1rXlk0KGSTTSeBosd0wYUKEmEq+jKBVVfVdj9XR0bsey6E1QzE1Z44kqv/xHzLFGQ5LT9Gbb3Y6ssQRDA4+WtazuSPGNEkbB4oWwmk3wvpfgytJCtmmFUhi1lwJ0y4cOknrbICXvwueVOkwANBRDy99G658EDy6zlSpCcdieZVdNNJOBskYDH6CbKWWK1hIRtSU40i10DXgWM/UaRv+YyZpBsO5zOAldtJC59HjCymTVlZr1gysop+aKtOD+/dLNfxYCwYleXz6aVnsf8EFUmJjqJGw0bByJZx9tqxBS0/XEbTRtnIl3HmndGHoSdYaGqQmXXSLrBjRJG2cmHUFTDlfdnjuXwsH35FdmzOvkCRtqGny6jch6Ie0ot5jqXnQUgOHNkHpqYNfp5RKXI100BCVoAH48OAnwD7qWUjpcT9nFikDJjV7Ji97NgIcSzapXMki6milmxD5pJHWc21BgUw5RQuH5dZ/MX2s/OIXsiszJ0cW+z/4oCSTP/mJtFIaK8nJMi2qRt/KlfDWW/L/Y4/MTKlF58DuO03SxpGkNCg5RW4j1d02xAMWAu2jEpZSygEBQtTRisVSQEako8DI+AliIv9Ec+GiHf8QVw1vEllkkkwLnaThwwJtdFFC1nFtPnDjGnxH6hVXwIsvynqslBRJzvbvl56TeXknFPNJqayUeCoqetcvpaVJo/L16+H002Mfkzp5Xi/ceqs0l9++XTZrnHGGY18ENElLcIUL5E8bBhP5HAlHusLkzx3+2gPvwI4npINB2Rkw83IpkKuUclYtTbzOnqPtn1wYzmL6iMtU5ES6CEjLKPlgsFhChAYtdjsSHtxcyFw2Re3unE8JCygdnWK0c+fC174G990n00/Wyi/PL3/55J531y54+WXo6pK1baec0nfR+FAqK+XP6HONkfu7dg2epAWDUFMj06GFhScXtxo7Lpes9Vu8+NjnjjFN0hJcwTyYthL2vADeFKmbFuqGRddDetHQ1+18EtbeK2vZPD5497dSr+3SO6UNlVLKGd0EeS1ST6xngX2AEKvZQz7pR48NJxkviyjlHarx4saFCz8BCsmgnGEaeh9DCl5OZyqnM0a7klauhHPOkRG0zExpi3Qy/u//4Fe/kqlKl0uaml98MXzlK8ee2srNHfyccFimZvtbs0bWOrW1yTlLl8omgOEaqKsJT5O0BGcMnHkTVJwDla/KOrYpF0DRoqGvCXTAhgdlF2jPxgJfpnQ02PsizLkqNrErpQY6QAshwr1rtQAvbjrws59GZjLMt68o8yghhzR2c4huQlRQyjTyj46sxS2fTxqJn6zGRvjNb2QxeFIksQ2HZSPAxRfDggXDXz9vnmxWqKyUxfvGSImGrCwZkYtWWQk/+IE8Vloqo4DvvivN0H/yE600roakSVqCshbefxG2PAIdh6FoMSz5FOSM4LOtpUamRPvv/ExKhwPrNUlTyknhoy3QBwqNoB5ZD4NhEllMOsHpzXHvvffkgzIpauTR5ZLb+vXHTtJcLkm87r1XGqGDlMj40pcGrl96/nn5Mz0yDWGMVK/fulV2aVZUjM57UglHk7QE9d5fYP39kJInt0Mb4dmb4LK7Iat8+Gt9WTItGr2ODaSYbrru9lYqZiyWRjrooJusSB0xaTjedz1ZmDBgKEYXjY5Y8hBlRo6nen9uLnznOzKFGQrJSNlgDh/umwxC7/q11taRx6wmnDgf11YnIuiHzf8lCZUvQ7oSpBVBOATbHz/29elFUHa6jKj1dCfwRwrhzrh0TENXSkX4CfIi23mGrbzGLp5kE2t4nxSSWMZk2vHTQgctdNKKn4WUks0Y1udKNAsXSi2shqh2LR0dUjrj7LNH9hw1NbLW7MiRgTXcoi1bJs8dze+XtXBO1HdT44aOpB2ncBBq10udsuRsmLwCMuJsdKmzQTYH9J+u9GXCke2DXwNS5LbpfXlfZ9wE7nuh6nV5LDkHzrtVuxQoFSsbqKKO1qO1zCyWndSRSxqzKaKIDGpoxAKTyCZXE7Tj4/XC978vt5oaGdlKSoJvfevYBWIDAfj5z6UlldstuzZPOw2++U0pD9LfeefJpoQdOySZ6+6WJO0LX9Cem2pYmqQdh1AAXv2BFJN1JYENwuY/wnnfia+isCk5MnoW6gZ31Ah7dytMWjbwfGth48Ow9c/yOWXDkD9H3tfyL0g9tbQicI2shZ9S6iQFCbGPetKjis0aDKkksZNDzKSQbFJ15OxkhMOyFuw//xN27pTEa/bsoadBoz3+uNRI62nCba0UQP397+GGGwaen5wMP/oRrFoFr78uOzovuwwWDbODSyl0uvO4VL0ONWsgs0JGzzLLISkD3rxDErh44UmG+R+D1v2yU9NaGSWzFuZ8ZOD5NW9KspleLK2jMsqkGfuaX0ByFmRM0gRNqVgKYwljB1QXc2EIEPv+gQnF75d1ZAsWyA7NT31KRtUWLx5ZggbwxBPSdLunRlrPRoC//U0+aAeTmgof/CDcfjt84xuaoKkR0ZG041D1uuxwjN4tnZQuyVDTPshzqEtHd7vs5KxZIy2fZlwGC66TDgVbHoGWKqmXtvSzg09X7nxKkk1X5L8GYyQx278Wuppk+lMpFTtJeCgknQY6+pTa6KCb+cTZ+orxxFq49loZzUpLkyTrhRdgyxZ45pmR98Hs7By4g9PjkQQwHJYpUKVGgSZpxyEprbdafw9r5eZUo/JABzz/dWjcK4lWnR/2Pg9nfhVmXwWzrpTpy+FGwrrbpX5aH0aSteAQHWL8LbDnWTjwrowqzrx8ZOU9lFIjcypTeIHtNNOJG0OIMNmkMpfYN3lOGOvWwerV0kaqZxTM54MDB2Sq8uabR/Y8K1bIqFl51Fb5Q4ekA4ImaGoUaZJ2HKZdCHue67vWq6NOkpPMY5S1GCt7X5QELWty77FAJ7x9H1ScK8mjOcZnxuTzYMP9kuT1jBJ2Ncnu0LRBOpd0NsKzX4G2Q3LNoU2w+xlY8d34Wpun1HiWTSofZCGV1NOCn3zSKCcHD5oEnLD163tLX/Touf/uuyN/nk98At55B6qqZLNBICA7RT/zmdGPWU1oI0rSjDFea22g37F8a+2RsQkrPhUulCnDdx9CSkaGIbMMzr3FuYLR+9dKohTNmwJdDdBSDbkzjv0cMy+DqldlHZrbB+GA/HnmTYO/rx1PSIKW1VN/MUdG1tbeAx9+qG9tNaXUiUvGy+wYjpxJ/06pvzYq/TZj7cgRWLtWph0XL4apU/t+iPWMfFnb93goBDOPY71KXh7cc49Mm+7eLRsQVqxwrAm3SlzDJmnGmA8Avwd8xph3gBustfsiDz8HLB3b8OKLMTDvaph6ATTslunP/Dljk5R01MPByBe74iWy1mwwqfkQ6jclaa1McfZP3obiTYULfwI1b8moWFoRTD1/8FE0gP1rpCRHNF8mtNZAx5Ghr1NKxSeLZRsH2MYBugmRTQrLqDjhZuuOWLMGbrtNRrV6krBrroF//MfehOz882VHZlWVJFQ9xWRTUgbflTmc1FRpH3XxxaP+VpTqcayRtJ8Al1hrtxpjPgo8b4z5pLX2LRiPX7NGR0rO2E7rvb8K3ryzd/2bywNnfFkapfc38zJZGxbokGTLWkmWJi0bvoF6fx4fTFkht2NJyZORNKKSwHAIMODVkj9KjTub2M9m9pOOj2S8dBBgFTu4hPnkMQ7+Und0wI9/LDXIeuqOBYPw6KNw5pkwd64cS0uDP/wB/uVfpC1UMChJ2113SU9NpeLMscaAkqy1WwGstY8BHwZ+Z4z5CBxHkzg1Yh1H4M2fQ0quTCdmVcjPb90pj/WXNwvO/oa0bGrdH0nQlsNZXx+7GOdcBd1tvZsKbFhee+pKGV1USo0fQUK8x0EySMaDG4MhBS9uDNs56HR4I/Pee1IgNrowrMcji/h7+mr2mD1bdnI+/jg88gi8/LIs+FcqDh1rJC1gjCm21h4EiIyorQSeAqaPeXQT0IF3ZFTKE1Wux5Msxw5sgOmDjKxPWQHlZ0obp6T0sZ9uLFkGp30R3v0tdHbL6N2UFbD8c2P7ukqp0ddFABvVB7SHFw/NdAxxVZxxDTHeMFg5jNZW6RawZo1cl5Qk9cumT5cSHDNnOrfIWKl+jpWkfRMogt6vU9baGmPMCuDGsQxswjrB8Ul3UuxKYBgDs6+EaRdBa63UURtqzZxSKr6lkIQHN0FCfXaO+glQRs4wV8aRefNkjVhLS28Pze5u+bN/H8677pIErbRUkri1a6W47Jw5kJ4Op5wC3/62PJ9SDht2utNa+4K1dqMxJs2YPsvjW4E7xza0ial4idQ0i65PFvTLF77iJc7FNRhvCuRO1wRNqfHMjYvFlNGGny4ChAjTShcuXMwZLzXZfD649VZJzKqr5VZXJyUxZkRtca+vl/ZNpaXyobp7NzQ1SULW3S27PzdsgP/+b+fei1JRRlon7UXgQqAtcj8V2d151lgENZGlFcJp/wJr7+67ceDUfzmxaUwbhsPvSYmM3Om681KpeHWYVvZyBD9BysimgtyY1USbSSE+PGzjAO10U042CyglkxG2SYoHCxbAww9LvTO/X+4X9vvAa2vrWyetuloStEAAurrkseJiePpp+Od/jv17UKqfkSZpydbangQNa22bMUbHgsfIjEtk1OzgO7Leq2Tp8e3U7NFeBy/dCs1VHN2LO++jsOTTuuRCqXiyizrWsg83BhcuqmhgD0e4gNkD1oqNBYNhMnlMZpwPi6elDZzejDZpkkxptrfLueGwfBgGAtKLE2QNWzA49HMoFUMj/dvfbow5WhPNGLMc6DzZFzfGXGqM2WGM2W2M+eYgj/uMMY9EHl9jjJlysq85XqQXwYxLpcTGiSRoAKt/KmvGMsul6G56MWz9k9Q5U0rFh26CrKeSNHykk0wqSWSRwiFaqKbR6fASi9cLX/wiNDRIK6isLJkCTU/vLcFx8KDUU1MqDox0JO3LwKPGmFpkafsk4NqTeWFjjBu4F7gIqAHeNsY8Ya3dFnXaPwON1toZxpjrgB+f7OtOFO11cHgbZESV/nF5wJsOu/4GZbrjXKm40EgHFosn6juzweDFTS1NTBnvo1vx5txze6c0Kyth82Y5XlcnI2ulpfAP/+BsjINpboZXXoH335cdqCtW9C05ohLSSJO0zcCvgEuAFuBJYOtJvvZpwG5r7V4AY8yfgKuA6CTtKuB7kZ8fA+4xxhhrrdZoO4ZQQGY4+09rGjcET3oMVCk1WpLwYJGq/9GtmEKEScbrXGCJbOZM+NKX5OeuLtnhWVUlGwdOPx2S42wtXk2NNH9vbJRNEs88A3/+M/zsZ5Cf73R0agyNNEl7GEnObovc/zjSLuqak3jtUqA66n4NcPpQ51hrg8aYZiAPmFA9Q09ERom0d+pqkhIZIOvb/M0w5XxHQ1NKRckmhRxSaaKTdHwYDAFCAExDfwGPueRkOO88p6MY3gMPyKaHioreY9XV8Mc/wr/+q3NxqTE30iRttrV2cdT9l4wxG0/ytQdbut5/hGwk52CMuQG4AaAi+j/iCcy44Kyvwapvy8YBl0cap5cslc4Ax8taOLwVDrwrpTcqzjnxtXJKqV4Gw3nM5HV2U087BvDg5hymk83x7c8KE2YXdeyijiBhppLPHIrxjfijXsVMfb2sjZs0afhpy1BI6rqVlfU9XlQEr76qSVqCG+nf3HeMMWdEenZijDkdWH2Sr10DlEfdLwNqhzinxhjjAbKAhv5PZK29H7gfYPny5ToVGlEwDz70G6h6TVpKFS6Unp6u4/y8tmFY80vY/Ywkf9ZKt4Fz/w3KdW2bUictDR8XM48WuggSIouUEyq/8Rbvs5cjpJCEC8MW9rOfJi5hXkx2iaoR6OqCu++WdlQul9w+8Qn42McG33ZvjIz2BYPSHaFHMKgFdyeAkf6tPR14wxizzxizD3gTWGGM2WyM2XSCr/02MNMYM9UYkwRcBzzR75wngE9Ffv4osErXox2f1DyY82FY+hkoO/34EzSAg+9KgpZZLresCplCfeOn0jNUKXXyDIYsUsgj/YQStGY62Uc9WaTgw4MXN1mk0kg7+2kag4jVCXnoIXjxRRlBmzQJcnPhwQfh9dcHP9/lgssvh9pa+YYMssHh0CG48srYxa0cMdJf2ZeO9gtH1pjdCDwLuIEHI71Bvw+ss9Y+Afwn8HtjzG5kBO260Y5DHVv1amk7Fd1zwpsKXY1wZAcULx76WqUSlcUSivS8NIOuzIit5khVpP6xuHDRQDsV5DoRlorm98Pf/tbb8QBkdCwrC/73f2Xn6WA++UkpGfLWW3JdOAyXXAIf/nDsYleOGFGSZq2tHIsXt9Y+DTzd79itUT93cXKbE9QocCfLlOegj+nmMzUB1dLEO1TTRCc+PMynhDkUO5qspSJTYf13iYaxpONzKiwVze+Xacr+Td99PlmfNpTkZGl7VV0tI2g9o3Aq4ekiBXVMU1ZAOASh7t5jnQ2Qkgt5s5yLSyknHKGNl9lJJwEyScaNi/VUsXXAktrYyiONPNJopYswFoulHT/JeCg/zlG0Fjo5RAtdBMYo2gkqI0N2aDb1m35uaIAzzzz29eXlsHy5JmgTiG75GQfqd8LOv0LbQShdDtMvAV9m7F4/bxYs+xy884AsiTCALwtWfPfE1rgpNZ5t4wAuXEdrmHlxk04y2zjAXEocW6BvMJzPLNZTRSX1WKCITJYzecS7O7sJspo9HKAZg8EC8ylhEaVxMaU77hkDN94It9wC+/dDSoqU1igogKuvdjo6FYdMoq3DX758uV23bp3TYYyaqtfhtR9KMuRJkUbpGZPg0jtjm6gBdNTDke3gSYaihbJOTamJ5kk2EsTi7be4v5UurmLx0WnHaBZLJwG8uAdcNxaChLBw3K/1JnvZyxEyScZgCBOmhS7OZcb47+sZT6qrpeNBTQ0sXAgXXwzZ2U5HpRxijFlvrV0+2GM6DhLHwkFYew8k50JSpIxOcrbUPdv1NCwYYhtFONIbeLRHuVLzoGKY3sVKTQT5ZFBJfZ8EKEAIL26SB/lIraWJt6mkHT8GwzTyWMrkMU3WTmR3aIAQ+6gnI5KggWw68OFlB4c0SRtN5eXwuc85HYUaBzRJi2NtB6G7rW//TZCpxv1rBiZpHUdg/W9k9M0YKD8bZl0B2VPBlxG7uJVKZHMpppIG2vCTgpcAIboIcDpTcfWb6mykg1fYSRIeMkkhjGUXhwliOZvpDr2DwQUjK9n6T2q6MfgJDn1hOCxtlVatkvsXXginnjp4zS+l1HHRJC2OJaXLGjAb7lv+ItQFqQV9zw364flvQNshSCuGpj2w5i5Y/2spYjv7SjjlH3UNmVInK5tULmEem9nPIVpIx8dpTKGcnAHn7qIOMPgi69dckVpoldRzCuWDTo2OlQAhdlNHJQ14cDGTQirIPTpqlhxJJDvpJiUqrk66mU7p4E9qLfzyl1JWoqew6quvwlVXwRe+MNZvSamEp7+y41hyNkw+D/a9DJllkqgFOiUhm92vhmHtOmg7IMVmW6qhYbeMuAW7wOWGbY/KaNpQU6RKqZHLIZXzmHnM81rpGjD1aCL/+AnGLEkLEeYldlBHK8l4sVheYzdzKWYZk4/GdTpTWMUOWujEjYsgYTJJYTZD9IDbtQuefVZ2LPbU/QqF4Mkn4bLLYMqUmLw/pRKVJmlxxt8qxWPb6yBvNiz/PNiQHMPIov0zb4LCBX2vaz/UW4y68X3w+CQ5w0IoAOklsO1/YP61fWchOurh0CY5t3hJ7DcjKJXIisjgIC2k0FtQMEgYFyamtctqaaKOVrJIOTpy1rPWbDZFpJMMQAEZXMEC9nKEFrooIpPJ5JI01K+KHTvkg8cVNdTvdsuxHTsGJmk9U6MvvCA/X3ABnHVW3+uVUkdpkhZHmvbBC9+UHZwYmeYsnA8f+L6UwOjZ2ekZ5LM9M9IF1VoI+WVa0yLPk5QGbp8kfuFgbwHaPc9JT86eQrXuJDjnFig7bezfq1ITwQwK2c1hWugkGS9BwnQT5FSmxGSXZ4/DtA3ojOCK/NxI59EkDSCdZBZRNuA5BpWePniCZYw81t9998ETT0hDcWNg9Wq46CL46ld1DZtSg9CvL3HCWljzC5nKzCyX6c3Mcji0GXb9VXZW5kwdPEEDKDkF8mdBSxX4siHQIZsOUvNk2rSzHgrm9iZorbXw1i8gJb+3J6c3HV7/oYzmKaVOXjJeLmEecykmCTd5pPEBZjOLwpN+7hBhamhkOwepoZEQQ7QFAdJIwjJ4uaXBdqT2EQ7D9u2wYQO09vtwWL5c1qI1NvYea2iAzExYurTvufv2wVNPydRoQQHk58vPL74IO3cOH4NSE5SOpMUJf7P0wcyI+gJrDKTkwd5VMO8YzbGMGz7w77DtMdj+OHQekY4AOTOg9YBMZy67off8/W/LCFp00peUBl0N0lB98hAt5JRSxyeFJE6hglOoGLXn7CLAKrbTeLRfJ2STwgXMOVpkN1oFuWxkP510H328jS6ySSWfQUa8etTWwve+J4VXjZFRs89/Xhp+g1TQv+02+NGP5BxrobhYirWmpPR9rp5ELHrkzeXqnRqdPfsE/20olbg0SYsTpmfmw0L0HngbGr5obFcTbHwY3n9Rrpt+CfzdH2QkbeeTUnw2ZxrM+hBklUc97xBfuu3R/1FKxatN7KeJTrLoTYSa6GQT+zmNKYAU0G2gnS6CZJPCSmbzJntpoQsLTCKL05k6dCcBa+H734fDh6Es8u3R74d77oEZM6CkBF5+GTZtgvPOg3nzoKgIJk8efAp0qKlRl0uSPSdZC1u2wEsvyXs891w47TRdK6ccp0lanPBlQOnpUPt2b100G4auRjjlnwa/JhSAF2+RtWxpxYCFHU9IG6lL7oClnxn69UqWSi4YCvROgfbsBC1cOIpvTCk16t7nCGn9Nh6k4WMvRziNKXQS4BV20kD70cfnUszlLKCTAC5cfTYzDGrvXqiq6k3QQBqBu1yye3PbNhlpS02Fri5Za/bDHw6d2CxdCllZcOQI5OXJyFxDg6xPO/XUE/1XMTr+9Cf43e8gKUnif/FFWLkSvvY1XSunHKVfE+LIaTdCVgW01MitdT/MuBSmXTj4+QffkZ2cmeWSaLmT5Of6HVC7AXY/Cy98C17+f1DzVu/uT4DsybDkn6D9IDRXSheDzno48yuQMrDck1IqjgyVNvQcX8NeGuggg2QySSGdZLZygBoaScN37AQNJPFyuQYmKV4vrFkjCVpFhawtKyuT43ff3feDJlpyskyN5uXJ1GhNTe906WCbDGKlrg7+8AcoLZXRwaIi6QiwahVs3epcXEqhI2lxJTUPLr8HDm+DzgbIniJJ21BaaxkwNWmMrPN96+dSXsOXKTs6t/8FPKmyIWHKCljwcZh/DZSdAQc2yG7QScshfYhySEqpXj2L8J1qOj6dArZzkMxISQ2LpQ0/symkiwC1NPdr72Tw4WEndVSMtL3T9OkystTR0Vuo1lq57/VKshUtJwcqK6G5ubcPZWcnPPqo1FILh2Un5x13yAiatX3rqzll+3b50xP167AnOd20CRYsGHhNIADvvSc14ebOlQRUqTGgSVqcMa6BNdCGklHatxMByOdedysE2iB/nnzOHHxXWkZZZARt19/gwDtw+d2yTi16rZpSamiNdPAOVRyghSTczKGYeZTgjvGkxEJKqaedw7QdTRMLSGcRZQQIDXqNC0NgmB2gAyQnw1e+ArffLkmVxyOja2edJbs5a2tlqrJHKCQ10nyRadieNW0bNsjolMcDf/4zbN4MP/1p36RotIXDsH69dD/weuH886WR+WBTlz0JaH/WDj7C99578r5aWuS+3y+Fe884Q3a7+mJX/04lPk3S4lzQDw27ZGNB3sy+bZ1KTpG+nI17Ib1YjrUdlHZSLo98HnW3SSeCcGx0mgAAIABJREFUpAzZTBDolNG05iqoWg3TL3LmfSk13rTj5wXeI4wlk2RChNlIDZ0Eji7Wj5UkPFzEXOpopQ0/6fgoJAODwYubTJLpJHC0vZPF0kmAeZQc3wude65sBHjpJUlKTjsNli2DN97onab0eiWhqa2FSy7p3dW5bRts3CjX9yRHFRWyk/PddyWhGQvRraqSk+X+00/D9dfLrb9Fi2QUsL6+d3SwtVVGEc86q++5HR1w662SjJaUSAJ68KCMuC1aJFOmP/oRTJo0Nu9NTTiapMWx/evgjR9Dd4fcT8mDxZ+URCspDUpPhZU/hE1/gL0vyKjanA9DSja8+zu5JtAOmN7PyJ5NAm4vNO4BNElTakR2c5gAITIjOyo9uMkkhd3U8f/bu/Pwussy/+Pv5yzJyZ40Tds06UoLLV1CIS1lEZSlIFREoZdV1ALDID8UnWvAQQWxPxyV3yDjjCPgIDKgosAUEdxAClQEC12ge+hC1yRtkibNvp5znt8fz2maNEuzn5Pk8+I6V3q++Z7vufO96OmdZ7nveUxs1+9yKBgM40nt0LDJYFjMdF7jA6ppwIMhSJgskpnRl/pskyfDihXtj33kI/CFL8Czz7rnoZBLaG5tU+ensDASUJvRq+PrMQoLBy9J27mzY6uqYBB+/WvX/H3ChPbnx8XBd7/rHoWFLsakJPjOd9x6u7Y2bYK6Ordmbf9+t54tNdUd8/vdCOOPf+xGH0UGgJK0GFV/FN78rhsVSx0DWFfb7Hcr3HSo8YI/ES75V1j0Zfc4rqECtj7jynP4Etwu0eZ68Ce52mng1qmlTYnKjyYyLB2jvkOXAE+kD2fdSU3Jo20syXyC+eynnDqaGEcKuWQM3LSsMW5U6hOfcInNmDFuZKmtcV0khB6Pm/4cLFu3nnif445PrRYUdEzSAKZNg8cfhw8/dAndjBku6TpZU9OJPxcWuqnN40loKOSSty1b3Khjaic99mpq3KaLigpXF27evOivyZOYpiQtRhW+A6Fml6QB1Je5xM34XLKVNM4lYX+93/Xj3P+6S9xmXAnTL4VLvwdvPwi1JeCLg3AIJuS5kf+6I24H5+QLo/szipyKxVJJA00EySCB+J7sShwkmSRRTCVtS7SGI1sIhrIPZ08lEtf76c3eSktzj87Mn+82H3z4oUvgjIHDh92U4DnnDF5MbdfJnayr9WfgkqWZM7u/9pw57udoaTlx7Phu1jFjut7ZCq6kybe+5RK44+ctXAjf/nbnCaEIStJiVnNd+1mCmmJXwywcdqNgAPFpLjmrLnTrzGwY1v67a5h+/l3wyZ9DdRGEW1yfzj0vu/6fuefB2f/garOJxKp6mvkbuymnLjJeBXnkMnuwE48unEYWOymhlkYSiCNEmHqaOZMJnVb5H/W8XjeF+MQTrpyFta7o7S23uCnGwXLeefDYY27U6niR3IoKl0yedVb/rj1uHNx4o/uZEhLcdGd8PEyf7kbOjhyBBQs6jqJZCz/8oUvuJrVptPzuu64m25VX9i8uGbGM7S7zH4by8/Pthg0boh1Gv5UVwCv/7JIv44GSzZGSG8CUj4I/wY2sHXwTpl7i+nOCS9RqCuGqh12ngbZs2H0ueIaur7NIn62mgDJqSI6UkggRppZGLmU2E+hkKmkA1dLILkoppYZ0EpjFBNJJpJpGtlBIEZUE8DOL8ZzO+KiV4hg2QiH34TOYOzrb2rzZLeCvrXXvm5XlRqxOO21grr9rl9tM8cc/ugTweDeFCRPc+548nVtSAjff7EYR2/72XVnpkrYf/nBg4pJhyRiz0Vrb6SJNjaTFqLGz3NTlnj+DNx48cW535rh5LkEDV+zWEweBNrMNxgMYV+T25CTNeLougikSS2ppopSadrW+vHjw4WUPpYOapFXTwCvsoIUQ8fippJ59lHMpsxhHChcyY9Dee0Sy1nUZaG52ScpQrMHKy4Nf/hL27HEjejNmDOz7nn66e9x6q9uosH+/2yF69tmdT116u/jNOBweusRVhiX93xGjjIFz73Drxg697Upq1JVB4VpXPsMY8AVgzGl0mnmpa4AMZ0FCGDoWi/VgaCI4qO+9lWJChFt3ccbjo4FmNnKAK5nTIaYwttNYBTcd+OCDrnK/x+NKXNx5p1uvNtj8fldodjAZA7NmuUd3MjPdOXv2nNi4EA679WlXXDG4McqwpiQthhkPTDzHPY6rOuiapvuTIPMMePmrUFcKiVnu+3VHIGUijB+Cz0CRwZJKgDh8NBEkPvIxZbE0EWQyYwb1vQ9T1WGnZgA/FdQTIowvssOznmbe5xAHKMdgmEYmZzFJ69OOC4dh5Uq3CzI31yU0VVWuzthjj3W9+3MkMsb1Af3Wt9z9OL7M6Kqr4OKLoxubxDQlacNM2uT2raIu/QG88yPXVB1gwllw7tfaF70VGW48eFjMNN5kD0204MVDCyHGkcK0nrY16qME/DRE3vO4EGH8eFuPhQjzGh9QSyPJBADLhxzlGA1cyZkaVQO3buvAgfYN2tPS3OjRX/8Ky5ZFL7ZoyM6Gn/3MrZerqnLr46aoDpJ0T/+UD2PBJrfT88JvuqTM4z2xgUBkuMshg6uZy16OUk8L2aQyiYzWkaz+aCbIPo5yhGpSiOc0xpEWmd6cw0TeYg8+vPjwECZMLU3kkduafBVTSQ2NrVOiYEgjgWPUUULNoG9sGBaqqztvw+Tzuer+o5HPN7jlR2TEUZI2TO1dDRt+CsFGN3KesxDOuzPaUYkMrFQSOIuBbS7bSAt/oYAaGvDjo4hKdlLKRzmdbNKYwhgamMwWimiIrDc7k2zmtCn9UUsTXe2Lr6Opi++MbC2EaKCFRPwukT7tNPfhFAyeWBxvrdtAkJcX3WBFhgklacNQ6Xb4+0NuHVpCpiutUbTOTXtefF+0oxOJbbsooYZG0jhR2LSRFtaxn2twiznHk8pcLEFCTGEs6e1K2NJmBO0EG0nbUgkMYvSxx2LZRjE7OEyYMB48zCGbOZkTMZ/9rNtlmZTkdjhWV7s6YoPVEkpkhFGSNsRaGuDQWtc3M20yTL7gRFeBntr9R/DGnSjFYTyQkuO6FNQfhcSx3b9eZDQrjNQ4ayuAn2oaqKOJXZRSwBGIJF0FHOEjzCCHE1ums0llDElUUEcScVjcCFo2aYyll3+hh7ldlLKZQlII4MVDiDCbKCQeHzNvuMG1P3r5Zdec/KKL4JJLVGFfpIeUpA2hhgr4y9eh9rBr4WRDsOWXcPmDkNKLIur15a78RlvG4x7NdUrSRLoTj4/ak6Ykw5GErJIGCjhMCgl4IuvPmgnyOruYTAa1kT6Ys5jAJZzBdorZy1EMMI8cziS7X5sGjrfBqqOJFAKt6+Ri2Q6KSSSudVOFFw+JxLGDI8w0413ro4ULoxylyPCkJG0IbXkaao9AapslNjXF8P7P4aJ7e36d3HNdB4K2mwRaIg3UUyYOXLwiI9EZjGcNu4jHhxcPFksNjUwjkxKq8eBpTdDArbUqoTpSOy3AbkrZTzlXMoezmczZTO7m3XquhRBvsYfDVOGKH1omM4bzmD5wjdEHmMXSQAspJ03x+vCO2rV5IgMpNv/mj1AH3nSN0dtKngCH/u7WlfXUaUsgfSpUHXCjc7WHoaEcFn0FvJpFkGEgRJgDlLOWvWziEJXUD9l755DO2UymgWZqaKCGRnJJJ58pHUbBLJaj1OIBkogjDh+pJBAkzHaKBzSuLRRSTBUpBEglQAoB9lNOAYe7fE0zQcqpo57mAY2lpwyGcaR0eP8GmhmPmgOL9JdG0oaQL86VzPC2qZPZ+rwXMyRxybDkIbfDs2gdJGXBzKshc+aAhywy4IKEWMMujlCNHy9hwuzgMB9hBpMGuVAtuMTiTLKZQRbVNBLAF6l1BpMZQwFHCBHGi4cwlkaCxOFrt44tAT+HqR6wmCyWPZSRTHxromgwJBHPbkqZS06H8z/gCJspxEa2LExhDOcybUBKlPTGAibxKh9QQyNx+GgmiMH0blduU5Prg/nyy24H6JIl8IlPQGB0bcIQOZmStCF0+lJ4/0lIm+LKB1nrpj9nXdt5OaHuxCXBrE+6h8hwcpAKjlBNGgmtCUkzQd5hHxNJH7KpvTh8HRb5Z5LEWeRGkh8gUoIji+R2o2wthEhvszt0IIQIt5tmBdcGq5lQh3MLOcZGDrYu1rdY9lGOHx+LmDqgcZ1KJsl8nDns5Ajl1DGJDM5gfM/X01kL3/sevPuua59kDDz+OLz/Pvzrvw5Nr0+RGKUkbQjNvg4q9rpenMbjpjgnngN5X4h2ZCJD5xDHiMPXLumJw0cNDVRST2YUd0caDHOYyBQyKaUGHx7KqGk3utZCiCaCnEkvdvv04H1zyaCYytZRPXA7RmfQsX3SB5S0rqk7/vpUAnxIGQuYhH+IR9PSSGAR0/r24oIC2LDBVd8//ttqUpJL0rZtG5o+nyIxSknaEPLGwUX3QOUBqCmCpPGQMb33o2giw1k8PsK0X4R5fMpuqKfqOtNIC0Uco5IGMkliDtl48LCTI4SxxOHjPKaRw8C291jAJMqppYoGvBhCWFIIMJeOu4Eaae4w4mgwWCzBSAurYePAAdfns+0H4fGphgMHlKTJqKYkLQrSp7iHyGh0Gll8yFGChPFFpurqaCKTpKgXgq2mgVcpoJEgXjzsoYztxHM5s5nLRJoIkoB/UKZkUwhwBWdykArqaCaDRCYxptOEK4cMCjhCXJuP8CaCpBAgMNw+1jMzXaHbkxnjvicyig2zv80iMtxlkcJCprCRg4AbRUsnkQuZEfXG5Bs5SAuhduupqmlgG8UsYuqgjVA1EeQ9DrKfciyWLFI4jawu328WEzhIBdXU48dHkBBgYuIetlVBHbsooZIGxpPC6Ywnifj2Jy1Y4JqPFxfDhAnuWEkJjB+vPpcy6hlru+pANzzl5+fbDRs2RDsMETmFJoIco544vGSQGPXkIkyY37CBVALtYgkRppkgyxichMFiWU0BZdSSTAAD1EemM5cyr0N3hOMaaeFDyiihmlQSmEHWgG9m6I/DVPEGO/Fg8OOjmRb8+LiCMzvUVaOsDB5+GNatc8/z8+HLX3aJmsgIZ4zZaK3ttFeaRtJEJCri8TGB1GiH0cpg8EXKbnhPStIGc41XBfWUUUtKm+QwiXiqaeAgFZxO54lKAD9zmMicTtasRZvFsoED7UqXxOOjmgZ2cJhzT95kkJUFK1dCXZ17npQ0tAGLxCjtbRYRwSVpM8iihsbWZuluvVxzl4nSQGigOfLu7UcSDYYaGgftfQdTMyFqaCT+pHGABOIopqrrFyYlKUETaUMjaSIiEfPJpZYmiqiEyG7JGWQxaxCTtFQSOL6/tW2iFsaSyfBMWHyR5lonj0oGCUV+XhHpCSVpIiIRfrxczOlU0UAtTa3tmQZTKgGmM5Y9lJJAPB4M9TSTTgK5ZAzqew8WLx5mMZ5tFJMaaVYfbK0vNyHa4YkMG0rSREROkkZCzyvmD4BFTCWTJHZSSpAQc8hmNhNiom5cX80jhyAhdlMGgBfDQqYO28RTJBqUpEVJUw0c2+v6cKqgrcjoU04dH1JKPS3kkM5UxjJzEKdVh5oXD/lMZR65NNJCEnHDOukUiQYlaVGw8yV473FXUNuGYcxpcPF9kDg22pGJyFDYTzl/50M8GDx4KOQYeyjjMmYNr24BPRCPr8MGAhHpmajs7jTGjDHGvGqM2R352un4tzEmZIzZFHm8NNRxDobSbbD+UUjIhJSJkJIDx/bB337gkjYRiX0WSwV17OMopdS07gbtiSAh1rGfBOJIJkAicaSTSDm17Kd8EKMeBayFv/wFbr4Zli6Fb3wDdu6MdlQifRatEhzfAF6z1s4EXos870yDtfasyOOaoQuva+W7Yf0j8Lfvwd7XIdTcu9fvedn18PTGuefGQHI2HC2AmuKBj1dEBlaIMH9jN39mO2vZy2oKeIUdNNLSo9dX09hpf814fBRybDBCHj1+9zt46CFobHRdDHbtgq9/Hfbti3ZkIn0SrSTtk8BTkT8/BVwbpTh6Ze9qePlrsPvPULQR3v5/8Pq9vUvUmmvAc1IBcWPAeCDYMLDxisjA20kJBzlGKgFSSSCVBCqo430O9ej1LjmzHUbfQoRJ6KK7gPRASws8/bRrLZWSAh6PK5ILsGpVdGMT6aNoJWnjrbWHASJfx3VxXsAYs8EY844xJqqJXEu9G0FLzHLTlElZkDYFSjbDwbd7fp1JF7hEre3UZnMtxCW564lIbNtNKYnEtatplkyAfRwlRPiUr08hwHhS2xXNDRIijGVGlx+FckqVlW4ELXBSyZTUVNi9OzoxifTToK3mNMashk4L4tzTi8tMttYWG2OmA68bY7Zaaz/s5L1uBW4FmDx5cp/iPZVj+9yIma/N339jwJcERe/CtI/17DpTLnYjcke2gD/BXdMY+Mi94NUv0SIxL3xS0Vmg9VlP16ZdwGm8zR5KqMFg8GJYzDTGkjzA0bqYGiJTsYnEDfj1B01lJbzzDlRVwZlnwpw5bnSsK2lpEB8PTU3u63E1NTB//uDHKzIIBi1Js9Ze1tX3jDElxphsa+1hY0w2UNrFNYojX/caY9YAC4AOSZq19jHgMXAN1gcg/A7iktxOTGvbl8sIN0OgF2V/fPFwyb/CobVQvAESxsD0yyBt0sDHLCL900SQskgiNZ4UfHiZRibbKSatTTPzOpqYSHqPS0wE8HMps6mhkWaCpJEwKOUpqmjgHfZRTi0AY0nmPKYPeoHefisogHvvhfr6E8c+8hH4l38BXxf/bMXFwec+B//9326aMzERKiogHIbrrx+auEUGWLT2Rb8ErAAeiHx98eQTIjs+6621TcaYscAFwL8NaZRtpE2BMTPdiFryBJeotTS4xG16l+lo57xxMPVi9xCR2HSActaylzAWMPjxcBEzmU02h6mmgloMBosboTqH3o/iD2ayFCTEa3xAC6HW9zlGPa/xAUuZF7s1y8JheOABl4wdnxmxFv76V7jgAri4mw/OT3/aJWfPPAOFhTB3rtvpOX360MQuMsCilaQ9ADxnjPkH4CCwDMAYkw/cZq29BZgN/LcxJoxbO/eAtXZHlOLFGLjoHvjb990OT2NcsnX+Xa7OmYiMHLU08Xf2EsDfuguziSB/ZTef4iyWMJvDVHGMepKJJ5eMmKtvVkwVDbS065yQRDzVNHCE6tit/H/wIBw9Crm5J44ZA8nJ8MYb3SdpxsDHP+4eJ097iAxDUUnSrLXlwKWdHN8A3BL589+BeUMcWreSxsEVP4Kqg24jQca09mvUBkpTDdSVuOK2gfSBv76IdK+IY4RPKpMRj681wZlEBrmRR6xyJUE6rv5ou0ZtyITD8PvfwwsvgN8Pn/0sXHJJ5+d6I/e8w9qSsHttTylBkxFAZaB7yRhIH6RdmDYMW34FO1ZFdn9amPFxOOdWbSoQ6Ytj1HOQCkKEySGdcaR0WPTfmWA3uzTDPdjBGQvSI2vmbJuNDsc3Noxps55u0IXDsGIF/PnPJxb+/+538I//CN//fsfzc3PdNOeRIzAusts1FIKGBrj88qGLWyQGRKsEh3RizysuSUsYGynzMQF2vgjbn4t2ZCLDzy5K+DPb2E4xH1DCagrYyIEe7cDMJg0wkfVoTpAwBsM4UgYx6oGTRTKTyKCKBhppoZEWqmhgMmMYQ9LQBfLqq/DyyzBmDGRmukdaGjz+eOelMYyBb33L1TorLIRDh6C4GK67DhYuHLq4RWKARtJiSMHzLkE7Pmrm8bpuBB+8APM+p9F7kZ5qoIWNHCSJeLyR30Utlp2UMJWxpyx1kUEic8hmO8WRUSi3eWAhU0gYJmUsDIYLmEE2R9lLGQALyGUaY3s0mtiphgaoq4OMjBPTkqfy8svua9vzj09bvvwyzJzZ8TWTJsHPfw6bN0NtrTsnJ6dvMYsMY0rSYkhjJcSd9Eu6Nx7qSiEc1JSnSE+VRfppettMFpjIf4epOmWSZjDkkcskMiiiEg+GXDJapxCHCy8eZjKOmf0tktvSAk8+CX/4AwSDLkm77Ta48MJTvzaxi3tmLSQkdP49cIlcfn6fwhUZKTTdGUMmLoSGo+2PNRyFcXOVoIn0hreLjzYL+Hr4sWcwZJLMfHKZS86wS9AG1M9/7lorZWa6NWPWuvVk27ef+rWf+5xbi9bYeOJYXZ0rOPvpTw9ezCIjgJK0GDL/8+BLhOpCaDgGNUWAdRsHRKTnxpNCHL52Tc9bCOHBMCmGd2TGpPp6+NOfXHJ2fJoyOdklWb/97alfP2cOrFzpOgGUl7uHtfDII26dmoh0SdOdMSQ1B65+2DVwP/qBK/Ex8yq3iUBEes6Hl49yOn9lF9U0QKT10gVMJznWq+3Hmpoat0Pz5Er/iYlw+HDPrvF//g8sW+aSPb8frr7a9dQUkW4pSYsxiWMh7wvRjkJk+BtLMtdyFmXUEibMWJKJ00de72VmupGz+vr268sqK7svLHuysWPhi18c+PhERjBNd4rIiOXFwwRSmUi6ErS+8vngS1+CsjL3qKtzpTFSUuDaa6MdnciIpk8tERHp3sc+5taPPf+8KzL7kY+4BG1cP3eNiki3lKSJiAwgi+UQx9hOMTU0MYFU5pFDxnDfHZqX5x4iMmSUpIlIzLFY9nGUnZQSJMQUxnAGE4gf4I+sMGEKqaSQY8ThYzpj+12Nfw9lvMs+AviJx0cxVRRTxZWcObrLeIhIrylJE5GYs5EDfEAJAfx4MGyliEKOsYQz8dHDSvenECbMm+ymkEr8eAlj2UUJ5zKV0/pY/DVMmM0UkkR8a3P2ZOKppZECjnAe0wckdhEZHbRxQERiSi1N7KKUVBII4CcOH2kkcox6DnGsw/kthAgS6vX7FFFJEZWkkUAS8aQQIJE4NnCQZoJ9ir2RIM0EWxO04+Lxc5SaPl1TREYvjaSJSEypogEAz0n9Jb14OUot0xjbet4G9lNCDQaYQibnMJl4etaeo5BKvHjb9bH04aWBFo5Rz3h6X8crHh8+PAQJt+ts0ESQsaT1+noiMrppJE1EYkpCJMmy2HbHw4RJJh5wSc9qCjhKHSkESCLAfsr5K7tpiYxmnfz6kwXwESbc7piN/NfXKVUvHs4km1oaaSGExdJAC2HCzCa7T9cUkdFLI2kiElMySCSLFEqpIYUABmigBT9epkZG0Q5RQRNBUnENug2QRBx7KOMwVfjwkkkSC5lKZhcbAaYylgKO0EIIP14slnqaSSWBMf1Y4D+HiXjxsJ3D1NNMBoks4LRTNnUXETmZkjQRiSkGw0XMYAMHOMgxwlgySWIRU1tH2WppajdNCVBKDfU0kU4CKQSoppHVFLCUeSRFRuDayiCR8ziN9eyjkRYsljQSuIiZHa7d2/hnk80sJhAijBdPv64nIqOXkjQRiTnx+LmAGSwiRIgw8fjaJToZJLabqmwhSC1NePESwI/BkEgc1TSwl6PMI6fT95lGJrmkc4x6fHjIIHHAEiqDGbCdqCIyOilJExkhLJaj1LZO2aWTMOxHcPx4O+yUBMglgzEkUUEdicRTTzNBwowhsV37Jy+eSIP17t9jHCkDHruISH8pSRMZARpp4a/sopy61mOTGcN5TMc7AvcHefFwCbP4gCPs4yhJxJNGAmNPSraChMhSAjb4SkvhhRdg40YYPx4+/WlYsCDaUYkMe0rSREaA9zhIOXWtC+ldxf5yMkkasbsK4/GRRy555ALwPgfZzmEC+PHioZ5mkgkwlcwoRzrClZXB174G1dWQkQE7dsD69fCVr8All0CiuiyI9JWSNJFhLkiI/VSQTKD1mMG07nYcqUnayc5iEmkksJMSmggymwnMZkK76U8ZBC++6BK03NwTx3buhH/8RzjrLDj/fLj9dshUsizSW/r0EhkRbIfVZwZD6KQ6YCOZwTCdLKaTFe1QRpdNmyAtUqg3GIR166CpCfx+GDMG3n0XiovhJz8BrzZSiPTGyFusIjLK+PAygVTqaGp3vJ6m1ur8IoNm4kSor3d/LiuDxkYIBMAYiI+HnBw4eBC2bo1unCLDkJI0kREgn6nE4aOKBqpppIoGxpDELCZEOzQZ6T71KWhuhpoaaGiAUAjq6mDSJDeaBmAtlJdHN06RYUjTnSIjQCoBljKPQo5RQxNjSGQi6SNyZ6fEmNmz4d574dFHXZLW0gJnnOEe4BI0gMmToxejyDClJE1khIjDp/VYfdRCiFJqsFiySCFeH429c/75sHixm+584AG3w7OhwSVoR4/CRRfBjBnRjlJk2NEnkYiMaoep4m/sIUgIAA+G85nOZJXu6B2Px9VI+9734KWX4NVXweeDZctg6VK3Rk1EekVJmoiMWs0E+Ru78eIlkTjAjaq9zV4ySe6056ecQmIiLF/uHiLSL1qwIiKj1hGqCUZ6gx7nx0sYSxGVUYxMRERJmoiMYl3XkbOEsEMai4jIyZSkicioNZ5UoH2yFiaMwZAd+Z6ISLQoSRORUSuROPKZQh1NVFNPNQ3U0MRcJpKOek6KSHRp44CIjGqnM55xpFDIMSwwkXTGKEETkRigJE1ERr10EjVyJiIxR9OdIiIiIjFISZqIiIhIDFKSJiIiIhKDlKSJiIiIxCBtHIhhh9+HXX+AxgqYdD7M+DjEJUc7KhERERkKStJi1M6XYP0j4EsAbzy893PY+zpc8RD4tQlNRERkxNN0ZwxqroP3n4CkCZA0DgJpkDYFKvfBvjeiHZ2IiIgMBY2kxaCqgxAOgS++/XF/EhzeCKdfHZ24RERkdGlpaaGwsJDGxsZohzLsBQIBcnNz8fv9PX6NkrQYFEgDGwJrwZgTx0NNkDwhenGJiMjoUlhYSEpKClOnTsW0/QdJesVaS3l5OYWFhUybNq3Hr9N0ZwxKmQjZZ0N1IdhI3+emGvf1tCXRi0tEREaXxsZGMjMzlaD1kzGGzMzMXo9IKkmLURf8C0w6D2qKoaYIPB4k7f2mAAAULklEQVS46D5InxrtyEREZDRRgjYw+nIfozLdaYxZBqwEZgOLrLUbujjvSuA/AS/wuLX2gSELMsriU+Hib0PDMWipg+Rs8HijHZWMFCHCFHKMYqpIwM80xpJGQrTDEhHpsyeffJIlS5YwceLEaIcyYKK1Jm0b8Gngv7s6wRjjBR4GLgcKgfXGmJestTuGJsTYkJDhHiIDJUiINeziCNX48RIkxHr2k04iyQSYQRbTGYtXA+0i0kuF62DzU64aQfo0yFsBuYuG5r2ffPJJ5s6dO6KStKh8CltrC6y1O09x2iJgj7V2r7W2GXgG+OTgRycysh2kgiNUk0YCicRRRzPVNFFIJbU08i77eJsPsdhohyoiw0jhOlhzH9QfhZQc93XNfe54X9XV1XH11VeTl5fH3LlzefbZZ9m4cSMXX3wx55xzDldccQWHDx9m1apVbNiwgRtuuIGzzjqLhoYGXnvtNRYsWMC8efO4+eabaWpqAuAb3/gGZ555JvPnz+euu+4C4Pe//z3nnnsuCxYs4LLLLqOkpGQgbkm/xfKvyjnAoTbPCyPHOjDG3GqM2WCM2VBWVjYkwYkMV4c4Rhw+DIZGWqinmQA+DGAwpJHAISoopy7aoYrIMLL5KYhPg0A6GI/7Gp/mjvfVyy+/zMSJE9m8eTPbtm3jyiuv5I477mDVqlVs3LiRm2++mXvuuYfrr7+e/Px8nn76aTZt2oQxhhtvvJFnn32WrVu3EgwGefTRR6moqOCFF15g+/btbNmyhXvvvReACy+8kHfeeYf333+f5cuX82//9m8DdFf6Z9CmO40xq4HOCkbcY619sSeX6ORYp7/aW2sfAx4DyM/P16//It2Ix0cYt224iWC7v1QeDCbyV6+SBsaiPmQi0jOV+9wIWlvxqe54X82bN4+77rqLu+++m6VLl5KRkcG2bdu4/PLLAQiFQmRnZ3d43c6dO5k2bRqnn346ACtWrODhhx/mK1/5CoFAgFtuuYWrr76apUuXAq7UyGc+8xkOHz5Mc3Nzr8pkDKZBS9KstZf18xKFwKQ2z3OB4n5eU2TUm04WH1JGkDC+yGB6kBABfMThdqcYDAGVURSRXkif5qY4A+knjjVVu+N9dfrpp7Nx40b+9Kc/8c1vfpPLL7+cOXPmsHbt2m5fZ23n4zU+n49169bx2muv8cwzz/CTn/yE119/nTvuuIN//ud/5pprrmHNmjWsXLmy70EPoFie7lwPzDTGTDPGxAHLgZeiHJPIsDeOFPKZSgPNhAhjsRgM40nFArU0kkQ82aRFO1QRGUbyVkBTFTRWuhqfjZXued6Kvl+zuLiYxMREPv/5z3PXXXfx7rvvUlZW1pqktbS0sH37dgBSUlKoqXFFRWfNmsX+/fvZs2cPAL/85S+5+OKLqa2tpaqqiquuuor/+I//YNOmTQBUVVWRk+OGAZ96qh/zswMsWiU4PgX8F5AF/NEYs8lae4UxZiKu1MZV1tqgMeYrwCu4EhxPWGu3RyNekZHmDMYzlUyOUU8zQXZyhDJqgRbGk8K5TNPuThHpldxF8NH72+/uPO/O/u3u3Lp1K1//+tfxeDz4/X4effRRfD4fX/3qV6mqqiIYDPJP//RPzJkzhxtvvJHbbruNhIQE1q5dy//8z/+wbNkygsEgCxcu5LbbbqOiooJPfvKTNDY2Yq3lRz/6EQArV65k2bJl5OTksHjxYvbt68cc7QAyXQ0JDlf5+fl2w4ZOy66JSDcaaQEgQM/7yonIyFZQUMDs2bOjHcaI0dn9NMZstNbmd3a+Fp2ICKDkTEQk1mg+Q0RERCQGKUkTERERiUFK0kRERERikJI0ERERkRikJE1EREQkBilJExERkVHjvvvuY/Xq1b1+3Zo1a1rbSA0VleAQERGRAVHEMTZTRCX1pJNIHjnkkDHkcVhrsdbi8XQci7r//vuHJIZgMIjP1780SyNpIiIi0m9FHGMNu6inmRQC1NPMGnZRxLE+X/Puu+/mkUceaX2+cuVKHnroIR588EEWLlzI/Pnz+c53vgPA/v37mT17Nrfffjtnn302hw4d4sYbb2Tu3LnMmzevtbvAjTfeyKpVqwBYv349559/Pnl5eSxatIiamhoaGxu56aabmDdvHgsWLOCNN97oEFdFRQXXXnst8+fPZ/HixWzZsqU1vltvvZUlS5bwxS9+sc8/93FK0kRERKTfNlNEPH4C+DEYAviJx89mivp8zeXLl/Pss8+2Pn/uuefIyspi9+7drFu3jk2bNrFx40befPNNAHbu3MkXv/hF3n//fY4ePUpRURHbtm1j69at3HTTTe2u3dzczGc+8xn+8z//k82bN7N69WoSEhJ4+OGHAdeS6je/+Q0rVqygsbGx3Wu/853vsGDBArZs2cL3v//9dgnZxo0befHFF/n1r3/d55/7OE13ioiISL9VUk8KgXbH4vFRSX2fr7lgwQJKS0spLi6mrKyMjIwMtmzZwl/+8hcWLFgAQG1tLbt372by5MlMmTKFxYsXAzB9+nT27t3LHXfcwdVXX82SJUvaXXvnzp1kZ2ezcOFCAFJTUwF46623uOOOOwDXqH3KlCns2rWr3Wvfeustnn/+eQAuueQSysvLqaqqAuCaa64hISGhzz9zW0rSREREpN/SSaSe5nYt5poIkk5iv657/fXXs2rVKo4cOcLy5cvZv38/3/zmN/nSl77U7rz9+/eTlJTU+jwjI4PNmzfzyiuv8PDDD/Pcc8/xxBNPtH7fWosxpsP79aSneWfnHL9W2xj6S9OdIiKDraQEfvELuP9++N//hcrKaEckMuDyyKGJFhppwWJppIUmWsgjp1/XXb58Oc888wyrVq3i+uuv54orruCJJ56gtrYWgKKiIkpLSzu87ujRo4TDYa677jq++93v8t5777X7/qxZsyguLmb9+vUA1NTUEAwGueiii3j66acB2LVrFwcPHuSMM85o99q256xZs4axY8e2jsQNJI2kiYgMpt274e67oakJEhLgnXfgxRfh3/8dxo2LdnQiAyaHDD7K6e12d57HtH7v7pwzZw41NTXk5OSQnZ1NdnY2BQUFnHfeeQAkJyfzq1/9Cq/X2+51RUVF3HTTTYTDYQB+8IMftPt+XFwczz77LHfccQcNDQ0kJCSwevVqbr/9dm677TbmzZuHz+fjySefJD4+vt1rV65cyU033cT8+fNJTEzkqaee6tfP2BXTk2G94SQ/P99u2LAh2mGIiDh33gn798PYsSeOFRbCFVfAV78atbBEeqKgoIDZs2dHO4wRo7P7aYzZaK3N7+x8TXeKiAyWxkbYsQMyM9sfz8qCtWujE5OIDBtK0kREBovPB4EAtLS0P97UBGlp0YlJRIYNJWkiIoPF54Orr4biYoisiyEYhPJy+NSnohubiMQ8bRwQERlMX/iC2835+uvg9YK1sHw5XH55tCMTkRinJE1EZDDFx8Ndd8GKFXD0KGRnQ3p6tKMSkWFASZqI9Fk9zRRxjBZCjCOVTJIwdCwOKbjNAllZ0Y5CRIYRrUkTkT4pppKX2MI69vM+hbzCDtZzAMvIKusjIrGluLiY66+/vtevu+WWW9ixY0e35/z0pz/lF7/4RV9DG3AaSRORXgsS4m0+JA4vcbgijxbLLkqYRAbZaOeiyKi0bh089RTs2wfTprlp/kWLBvQtJk6cyKpVqzocDwaD+HxdpzWPP/74Ka9922239Su2gaaRNBHptQrqaSFEXJvf8wwGHx4OUhHFyEQkatatg/vuc2svc3Lc1/vuc8f76O677+aRRx5pfb5y5Uoeeugh5s6dC8CTTz7JsmXL+MQnPsGSJUsIh8PcfvvtzJkzh6VLl3LVVVe1JnQf/ehHOV7sPjk5mXvuuYe8vDwWL15MSUlJ6/V/+MMfArBnzx4uu+wy8vLyOPvss/nwww+pra3l0ksv5eyzz2bevHm8+OKLff7ZekJJmoj0moFO155ZwKs1aSKj01NPufp/6eng8bivaWnueB8tX76cZ599tvX5c889x8KFC9uds3btWp566ilef/11fvvb37J//362bt3K448/ztouikbX1dWxePFiNm/ezEUXXcTPfvazDufccMMNfPnLX2bz5s38/e9/Jzs7m0AgwAsvvMB7773HG2+8wZ133tmjhux9pelOEem1TJII4KOBFhLwAxAmTBjLZDJP8WoRGZH27XMjaG2lprrjfbRgwQJKS0spLi6mrKyMjIwMJk+e3O6cyy+/nDFjxgDw1ltvsWzZMjweDxMmTOBjH/tYp9eNi4tj6dKlAJxzzjm8+uqr7b5fU1NDUVERn4rUMwwEAgC0tLTwrW99izfffBOPx0NRURElJSVMmDChzz9jd5SkiUivefBwETN5g11U0QC40bU8csgiObrBiUh0TJvmpjjblpiprnbH++H6669n1apVHDlyhOXLl3f4flJSUuufezqq5ff7McaN+nu9XoLBYLvvd3Wdp59+mrKyMjZu3Ijf72fq1Kk0Njb29EfpNU13ikifZJLMteRxMTM5n+lcQx5zyVEJDpHRasUKqKpyxZvDYfe1qsod74fly5fzzDPPsGrVqlPu6rzwwgt5/vnnCYfDlJSUsGbNmj69Z2pqKrm5ufzud78DoKmpifr6eqqqqhg3bhx+v5833niDAwcO9On6PaUkTUT6zIeXXDKYxliSI7s8RWSUWrQI7r8fxo6FoiL39f77+727c86cOdTU1JCTk0N2dna351533XXk5uYyd+5cvvSlL3HuueeS1sc+ub/85S/58Y9/zPz58zn//PM5cuQIN9xwAxs2bCA/P5+nn36aWbNm9enaPWUGc8FbNOTn59vjuzdERESk7woKCpg9e3a0w+iV2tpakpOTKS8vZ9GiRbz99tuDtmastzq7n8aYjdba/M7O15o0ERERGTGWLl1KZWUlzc3NfPvb346ZBK0vlKSJiIjIiNHXdWixSGvSRERERGKQkjQRERHp0khbux4tfbmPStJERESkU4FAgPLyciVq/WStpby8vLUobk9pTZqIiIh0Kjc3l8LCQsrKyqIdyrAXCATIzc3t1WuUpImIiEin/H4/0/rZMUD6TtOdIiIiIjFISZqIiIhIDFKSJiIiIhKDRlxbKGNMGTC4HU8HxljgaLSDiHG6R93T/eme7k/3dH+6p/vTPd2f7vXm/kyx1mZ19o0Rl6QNF8aYDV316hJH96h7uj/d0/3pnu5P93R/uqf7072Buj+a7hQRERGJQUrSRERERGKQkrToeSzaAQwDukfd0/3pnu5P93R/uqf70z3dn+4NyP3RmjQRERGRGKSRNBEREZEYpCQtiowx3zXGbDHGbDLG/MUYMzHaMcUSY8yDxpgPIvfoBWNMerRjiiXGmGXGmO3GmLAxRrusIowxVxpjdhpj9hhjvhHteGKNMeYJY0ypMWZbtGOJRcaYScaYN4wxBZG/X1+LdkyxxBgTMMasM8Zsjtyf/xvtmGKRMcZrjHnfGPOH/lxHSVp0PWitnW+tPQv4A3BftAOKMa8Cc62184FdwDejHE+s2QZ8Gngz2oHECmOMF3gY+DhwJvBZY8yZ0Y0q5jwJXBntIGJYELjTWjsbWAx8Wf8PtdMEXGKtzQPOAq40xiyOckyx6GtAQX8voiQtiqy11W2eJgFaINiGtfYv1tpg5Ok7QG4044k11toCa+3OaMcRYxYBe6y1e621zcAzwCejHFNMsda+CVREO45YZa09bK19L/LnGtw/tDnRjSp2WKc28tQfeejfrjaMMbnA1cDj/b2WkrQoM8Z8zxhzCLgBjaR152bgz9EOQmJeDnCozfNC9A+s9JExZiqwAHg3upHElshU3iagFHjVWqv7095/AP8ChPt7ISVpg8wYs9oYs62TxycBrLX3WGsnAU8DX4lutEPvVPcncs49uCmIp6MXaXT05P5IO6aTY/otX3rNGJMMPA/800mzHqOetTYUWaaTCywyxsyNdkyxwhizFCi11m4ciOv5BuIi0jVr7WU9PPXXwB+B7wxiODHnVPfHGLMCWApcakdhvZhe/P8jTiEwqc3zXKA4SrHIMGWM8eMStKettb+NdjyxylpbaYxZg1vjqI0ozgXANcaYq4AAkGqM+ZW19vN9uZhG0qLIGDOzzdNrgA+iFUssMsZcCdwNXGOtrY92PDIsrAdmGmOmGWPigOXAS1GOSYYRY4wBfg4UWGv/PdrxxBpjTNbxnfbGmATgMvRvVytr7TettbnW2qm4z5/X+5qggZK0aHsgMnW1BViC2w0iJ/wESAFejZQp+Wm0A4olxphPGWMKgfOAPxpjXol2TNEW2WjyFeAV3ILv56y126MbVWwxxvwGWAucYYwpNMb8Q7RjijEXAF8ALol87myKjIqIkw28Efl3az1uTVq/ykxI19RxQERERCQGaSRNREREJAYpSRMRERGJQUrSRERERGKQkjQRERGRGKQkTURERCQGKUkTEeklY0ymMeYNY0ytMeYn0Y5HREYmdRwQEem9RuDbwNzIQ0RkwGkkTUQE10zbGPOBMeYpY8wWY8wqY0yiMWahMebvxpjNxph1xpgUa22dtfYtXLImIjIolKSJiJxwBvCYtXY+UI3rXvAs8DVrbR6uBU5DFOMTkVFESZqIyAmHrLVvR/78K+AK4LC1dj2AtbY60npKRGTQKUkTETnh5D551Z0cExEZEkrSREROmGyMOS/y588C7wATjTELAYwxKcYYbbgSkSGhBusiIriNA8CfgDeB84HdwBeAOcB/AQm49WiXWWtrjTH7gVQgDqgEllhrdwx54CIyYilJExGhNUn7g7VWJTVEJCZoulNEREQkBmkkTURERCQGaSRNREREJAYpSRMRERGJQUrSRERERGKQkjQRERGRGKQkTURERCQGKUkTERERiUH/HyOxP/RUzp2nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "The best k for KNN is : 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         1.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         1.         0.        ]\n",
      " [0.         1.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         1.         0.        ]\n",
      " [0.         1.         0.        ]\n",
      " [0.         0.         1.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         0.33333333 0.66666667]\n",
      " [0.         0.         1.        ]\n",
      " [0.         0.         1.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         1.         0.        ]\n",
      " [1.         0.         0.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         0.         1.        ]\n",
      " [0.         1.         0.        ]\n",
      " [0.         1.         0.        ]\n",
      " [0.         1.         0.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  KNN for iris data set with cross validation KNN模型用于数据分类\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Load Dataset: 加载数据     \n",
    "feature, flowerC = load_iris(return_X_y=True) \n",
    "scaler = StandardScaler()\n",
    "# print(feature[40:60])\n",
    "# print(flowerC[40:60])\n",
    "\n",
    "# ### -------------------------------- Data reduction --------------------------------\n",
    "\n",
    "# PCA to reduce it feature from 4 to 2 dimensions\n",
    "pca = PCA(n_components=2)\n",
    "feature_reduced = pca.fit_transform(feature)\n",
    "# print(feature_reduced)\n",
    "# print(\"Singular values are:\", pca.singular_values_)\n",
    "# print(\"Ratio of explained variance are:\", pca.explained_variance_ratio_)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "labels=['setosa', 'versicolor', 'virginica']\n",
    "s = plt.scatter(feature_reduced[:, 0], feature_reduced[:, 1], c=flowerC, marker='o', cmap='rainbow', alpha = 0.6)\n",
    "plt.xlabel('pc1')\n",
    "plt.ylabel('pc2')\n",
    "plt.legend(handles = s.legend_elements()[0], labels=labels)\n",
    "plt.title('Iris')\n",
    "plt.show()\n",
    "\n",
    "### ---------------------------- Split data into Train/test sets ----------------------\n",
    "\n",
    "test_size = 0.2\n",
    "train_feature,test_feature, train_flowerC, test_flowerC = train_test_split(feature_reduced, flowerC,test_size=test_size,random_state=3)\n",
    "\n",
    "### ------------------------------ Find the best choice of K for KNN --------------------------\n",
    "#  Use Training set !!!\n",
    "\n",
    "# KNN (k from 1 to 30) with 6-fold cross validation \n",
    "K = 40\n",
    "cv = 6\n",
    "k_range = range(1, K)\n",
    "k_error = []\n",
    "\n",
    "# iterations for k from 1 to 40\n",
    "for k in k_range:\n",
    "    knn = KNN(n_neighbors=k)\n",
    "    # 6-fold cross validation \n",
    "    scores = cross_val_score(knn, train_feature, train_flowerC, cv=cv, scoring='accuracy')\n",
    "#     print('Accuracy for each fold of cross validation', scores)\n",
    "    k_error.append(1- scores.mean())\n",
    "#     print('Test error for k =' + str(k) + ':', 1 - scores.mean())\n",
    "\n",
    "k_best = k_error.index(min(k_error))+1\n",
    "print ('----------------------------------')\n",
    "print ('The best k for KNN is :', k_best)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_range, k_error, 'g*-', alpha = 0.6)\n",
    "plt.annotate('Best K', xy=(k_best, min(k_error)), xytext=(k_best+2, min(k_error)*1.2), fontsize= 12, color= 'red', arrowprops=dict(color='black', shrink=0.04))\n",
    "plt.title('Test error for KNN with 6-fold cross validation')\n",
    "plt.xlabel('Number of K nearest neighbors')\n",
    "plt.ylabel('Test error')\n",
    "plt.show()\n",
    "\n",
    "### ------------------------ Test KNN with best K ---------------------------------\n",
    "#  Use training set to train knn, and evaluate knn on test set !!!\n",
    "\n",
    "best_knn = KNN(n_neighbors=k_best) \n",
    "best_knn.fit(train_feature, train_flowerC) # train knn\n",
    "test_predictC = best_knn.predict(test_feature) # predict unknown features to get their classes\n",
    "print(best_knn.predict_proba(test_feature)) # predict probability\n",
    "\n",
    "# plot the KNN training set and test results\n",
    "plt.figure(figsize=(10, 6))\n",
    "labels=['setosa', 'versicolor', 'virginica']\n",
    "s = plt.scatter(feature_reduced[:, 0], feature_reduced[:, 1], c=flowerC, s=220, marker='o', cmap='rainbow', alpha = 0.3)\n",
    "s = plt.scatter(train_feature[:, 0], train_feature[:, 1], c=train_flowerC, marker='^', cmap='rainbow', alpha = 1)\n",
    "s = plt.scatter(test_feature[:, 0], test_feature[:, 1], c=test_predictC, marker='*', cmap='rainbow', alpha = 1)\n",
    "plt.xlabel('pc1')\n",
    "plt.ylabel('pc2')\n",
    "plt.legend(handles = s.legend_elements()[0], labels=labels)\n",
    "plt.title('Classifcation results with KNN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a03404",
   "metadata": {},
   "source": [
    "### 混淆矩阵\n",
    "我们可以使用函数 [sklearn.metrics.confusion_matrix(y_true, y_pred)](https://scikit-learn.org/stable/modules/ generated/sklearn.metrics.confusion_matrix.html? 突出显示=混乱#sklearn.metrics.confusion_matrix）：\n",
    "- y_true：真实（正确）目标值。\n",
    "- y_pred：分类器返回的估计目标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e1cb0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(test_flowerC, test_predictC)\n",
    "print(best_knn.score(test_feature, test_flowerC)) # knn.score(),计算准确率的函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf98ad6",
   "metadata": {},
   "source": [
    "### 评估指标\n",
    "\n",
    "基于混淆矩阵，您还可以计算测试数据的精度/召回率/F1 分数：\n",
    "- [sklearn.metrics. precision_score(y_true, y_pred,average=None)](https://scikit-learn.org/stable/modules/ generated/sklearn.metrics. precision_score.html)\n",
    "- [sklearn.metrics.recall_score(y_true, y_pred,average=None)](https://scikit-learn.org/stable/modules/ generated/sklearn.metrics.recall_score.html?highlight=recall#sklearn.metrics。 召回分数）\n",
    "- [sklearn.metrics.f1_score(y_true, y_pred,average=None)](https://scikit-learn.org/stable/modules/ generated/sklearn.metrics.f1_score.html)\n",
    "     * y_true：测试集的基本事实\n",
    "     * y_pred：测试集的预测结果\n",
    "     * 平均值（默认=二进制）：多类/多标签目标需要此参数。 其他选择：\n",
    "         - “二元”，当他的目标 (y_{true,pred}) 是二元时；\n",
    "         - 当您要求每门课程的分数时，无；\n",
    "         - 当您需要每门课程的平均分数时使用“宏观”。\n",
    "\n",
    "准确度也可以通过 [sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True)](https://scikit-learn.org/stable/modules/ generated/sklearn.metrics.accuracy_score.html#sklearn.metrics 来计算 .accuracy_score):\n",
    "* y_true：测试集的基本事实\n",
    "* y_pred：测试集的预测结果\n",
    "* 标准化（默认=True）：\n",
    "     * False：返回正确分类的样本数。\n",
    "     * True：返回正确分类样本的比例。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16d9c9b",
   "metadata": {},
   "source": [
    "### 任务 1\n",
    "试着阅读并理解上面的代码，现在我们处理一个新的数据集 [dry_bean](https://www.kaggle.com/tomabraham3233/dry-beans-datasetcsv)。你们的任务是\n",
    "\n",
    "- 加载数据 \n",
    "    - dry_bean_train.csv*（用作包含特征和相应类别的训练集）、 \n",
    "    - *dry_bean_test.csv*（用作测试集，只有特征）、 \n",
    "    - 数据文件夹中的 *dry_bean_target.csv*（用作包含目标类别的测试集的基本事实）。\n",
    "- 对特征进行 \"标准化 \"处理。\n",
    "- 通过 \"PCA \"降低特征维度，并使用第一 PC 和第二 PC 对训练集进行 \"可视化\"。\n",
    "- 使用训练集 \"训练 KNN \"模型，找出最佳 K，并 \"绘制误差 \"线以直观显示最佳 K 的选择。\n",
    "- 使用训练好的 KNN 为测试集 \"预测类别标签\"，并 \"可视化 \"预测结果和测试类别。\n",
    "- 为结果构建 \"混淆矩阵\n",
    "- 通过精确度、召回率和 f1 分数来评估结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e62e395d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.84295737 -1.1451053  -1.30806532 ...  2.33957539  1.85870996\n",
      "   0.87715553]\n",
      " [-0.75923462 -1.00587726 -1.24617915 ...  2.38355701  2.14200589\n",
      "   1.03136927]\n",
      " [-0.7440636  -1.02581528 -1.17367579 ...  2.0204551   1.78155424\n",
      "   0.76182957]\n",
      " ...\n",
      " [-0.55975405 -0.66030626 -0.68646101 ...  0.54371392  0.38253774\n",
      "   0.70018797]\n",
      " [-0.5580278  -0.70719076 -0.73624651 ...  0.71992294  0.61928492\n",
      "   0.77898197]\n",
      " [-0.55523926 -0.67409017 -0.64673692 ...  0.42680195  0.23338154\n",
      "   0.65075319]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:667: UserWarning: The least populated class in y has only 53 members, which is less than n_splits=60.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "The best k for KNN is : 14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix:\n",
      " [[147   0  12   0   0   2   4]\n",
      " [  0  66   0   0   0   0   0]\n",
      " [  9   0 187   0   4   0   3]\n",
      " [  0   0   0 362   0   5   0]\n",
      " [  0   0   5   1 230   0   5]\n",
      " [  1   0   0   4   0 235  13]\n",
      " [  0   0   1  16   4   3 306]]\n",
      "0.9433846153846154 0.9441358153433966 0.9464317859985547\n"
     ]
    }
   ],
   "source": [
    "# KNN for dry bean data set with cross validation\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.neighbors import KNeighborsClassifier as KNN\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, f1_score\n",
    "\n",
    "# Load Dataset:     \n",
    "train_data = pd.read_csv('data/dry_bean_train.csv')\n",
    "test_data = pd.read_csv('data/dry_bean_test.csv')\n",
    "test_target = pd.read_csv('data/dry_bean_target.csv')\n",
    "\n",
    "feature = train_data.iloc[:,:-1].values # 训练集特征\n",
    "flowerC = train_data.iloc[:,-1].values # 训练集分类\n",
    "test_feature = test_data.values # 测试集特征\n",
    "test_flowerC = test_target.iloc[:, 0].values\n",
    "# print(feature)\n",
    "\n",
    "# # 数据标准化\n",
    "scaler = StandardScaler()\n",
    "feature = scaler.fit_transform(feature)\n",
    "test_feature = scaler.transform(test_feature)\n",
    "print(feature)\n",
    "\n",
    "# # ### -------------------------------- Data reduction --------------------------------\n",
    "\n",
    "# # PCA to reduce it feature from 4 to 2 dimensions\n",
    "pca = PCA(n_components=8)\n",
    "feature_reduced = pca.fit_transform(feature)\n",
    "test_feature_reduced = pca.transform(test_feature)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "labels=['BARBUNYA', 'SEKER', 'BOMBAY', 'CALI', 'HOROZ', 'SIRA']\n",
    "markers = ['o', 's', 'D', '^','*', 'v', 'p']\n",
    "for i, label in enumerate(labels):\n",
    "    class_indices = (flowerC == label)\n",
    "    plt.scatter(feature_reduced[class_indices, 0], feature_reduced[class_indices, 1], label = label, marker = markers[i], alpha=0.7)\n",
    "\n",
    "# ### ------------------------------ Find the best choice of K for KNN --------------------------\n",
    "# #  Use Training set !!!\n",
    "\n",
    "# KNN (k from 1 to 30) with 6-fold cross validation \n",
    "K = 40\n",
    "cv = 60\n",
    "k_range = range(1, K)\n",
    "k_error = []\n",
    "\n",
    "# iterations for k from 1 to 30\n",
    "for k in k_range:\n",
    "    knn = KNN(n_neighbors=k)\n",
    "    # 6-fold cross validation \n",
    "    scores = cross_val_score(knn, feature_reduced, flowerC, cv=cv, scoring='accuracy')\n",
    "#     print('Accuracy for each fold of cross validation', scores)\n",
    "    k_error.append(1- scores.mean())\n",
    "#     print('Test error for k =' + str(k) + ':', 1 - scores.mean())\n",
    "\n",
    "k_best = k_error.index(min(k_error))+1\n",
    "print ('----------------------------------')\n",
    "print ('The best k for KNN is :', k_best)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_range, k_error, 'g*-', alpha = 0.6)\n",
    "plt.annotate('Best K', xy=(k_best, min(k_error)), xytext=(k_best+2, min(k_error)*1.2), fontsize= 12, color= 'red', arrowprops=dict(color='black', shrink=0.04))\n",
    "plt.title('Test error for KNN with 6-fold cross validation')\n",
    "plt.xlabel('Number of K nearest neighbors')\n",
    "plt.ylabel('Test error')\n",
    "plt.show()\n",
    "\n",
    "### ------------------------ Test KNN with best K ---------------------------------\n",
    "#  Use training set to train knn, and evaluate knn on test set !!!\n",
    "\n",
    "best_knn = KNN(n_neighbors=k_best) \n",
    "best_knn.fit(feature_reduced, flowerC) # train knn\n",
    "test_predictC = best_knn.predict(test_feature_reduced) # predict unknown features to get their classes\n",
    "\n",
    "# plot the KNN training set and test results\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# labels=['setosa', 'versicolor', 'virginica']\n",
    "# s = plt.scatter(feature_reduced[:, 0], feature_reduced[:, 1], c=flowerC, s=220, marker='o', cmap='rainbow', alpha = 0.3)\n",
    "# s = plt.scatter(train_feature[:, 0], train_feature[:, 1], c=train_flowerC, marker='^', cmap='rainbow', alpha = 1)\n",
    "# s = plt.scatter(test_feature[:, 0], test_feature[:, 1], c=test_predictC, marker='*', cmap='rainbow', alpha = 1)\n",
    "# plt.xlabel('pc1')\n",
    "# plt.ylabel('pc2')\n",
    "# plt.legend(handles = s.legend_elements()[0], labels=labels)\n",
    "# plt.title('Classifcation results with KNN')\n",
    "# plt.show()\n",
    "\n",
    "# 创建混淆矩阵\n",
    "cm = confusion_matrix(test_flowerC, test_predictC)\n",
    "print('confusion_matrix:\\n',cm)\n",
    "\n",
    "# 通过精确度、召回率和 f1 分数来评估结果\n",
    "acc = accuracy_score(test_flowerC, test_predictC)\n",
    "recall = recall_score(test_flowerC, test_predictC, average='macro')\n",
    "f1 = f1_score(test_flowerC, test_predictC, average='macro')\n",
    "print(acc, recall, f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6526f896",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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